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The detection system of DDoS (Distributed Denial-of-Service) attacks aims to enhance network security across all facets of internet technology utilization. One is at SPKLU, which stands for Public Electric Vehicle Charging Station. The research employed a deep learning approach utilizing a Convolutional Neural Network (CNN) on a publicly available dataset. Based on our study and analysis, CNN has a precision rate of 95%. Its high accuracy and balanced performance across diverse attack types indicate the model's practical application in real-life situations. The model demonstrates promising performance in detecting different network traffic anomalies, offering significant insight into its potential for practical use. Further investigation is necessary to strengthen the resilience of DDoS assault tactics against emerging dangers and to tackle any potential constraints.

Hydroponics is a soilless farming technique that has emerged as a sustainable alternative. However, new technologies such as Industry 4.0, the internet of things (IoT), and artificial intelligence are needed to keep up with issues related to economics, automation, and social challenges in hydroponics farming. One significant issue is optimizing growth parameters to identify the best conditions for growing fruits and vegetables. These parameters include pH, total dissolved solids (TDS), electrical conductivity (EC), light intensity, daily light integral (DLI), and nutrient solution/ambient temperature and humidity. To address these challenges, a systematic literature review was conducted aiming to answer research questions regarding the optimal growth parameters for leafy green vegetables and herbs and spices grown in hydroponic systems. The review selected a total of 131 papers related to indoor farming, hydroponics, and aquaponics. The review selected a total of 123 papers related to indoor farming, hydroponics, and aquaponics. The majority of the articles focused on technology description (38.5%), artificial illumination (26.2%), and nutrient solution composition/parameters (13.8%). Additionally, remaining 10.7% articles focused on the application of sensors, slope, environment and economy. This comprehensive review provides valuable information on optimized growth parameters for smart hydroponic systems and explores future prospects and the application of digital technologies in this field.

This paper reviews sustainable manufacturing practices by integrating environmental, economic, and social dimensions of sustainability, emphasizing that environmental aspects are most frequently addressed (53.8%), followed by economic (34.6%) and social (11.5%) dimensions. Key findings identify crucial practices for sustainability across materials, products, processes, and supply chains, particularly sustainable materials derived from natural, renewable, or waste sources. An analysis of 17,694 articles highlights trends and gaps, linking practices to life cycle stages and Sustainable Development Goals (SDGs), notably SDG#9 and SDG#12. A proposed framework emphasizes continuous environmental performance improvement through quantitative analysis using the Life Cycle Engineering (LCE) framework, enhancing competitiveness and reducing environmental impact. The LCE framework case study demonstrates how waste materials, like plastic bottles, can be repurposed as raw materials, illustrating its value, especially for small and medium-sized enterprises, and highlighting the importance of integrating sustainability from the ideation stage.

The Public Private Partnerships (PPP) and the Private Finance Initiative (PFI) have been significantly adopted over the last three decades as a mechanism for the delivery and financing of projects. Various infrastructure project types in the energy and the transport sectors have adopted this procurement system for their ‘anticipated’ effectiveness in risk distribution and value for money. Yet, their acclaimed benefits, particularly in ensuring value for money in the waste to energy – or energy-from-wastes (EfW) – sector are under significant scrutiny. Governments and investors are increasingly questioning their fiscal efficiency and overall benefit to taxpayers. Despite these concerns, studies investigating whether PFI provides value of money of PFI in EfW are lacking. This study addresses this gap by comparing the PFI models used in the UK and Canada in currently operative EfW projects using mixed risk epistemology approach combining methods that provide both a real and objective evaluation of issues and risks and a subjective (or opinion-based) evaluation. The former is achieved through a quantitative probabilistic model for simulating EfW feasibility at the operation phase. The quantitative probabilistic model is capable to model and capture how operational phase costs and profitability are affected by lifecycle risks (such as quality of service driven by contractual requirements that are usually passed on to the Operation and Maintenance (O&M) contractor) and a range of technical, payment and incentive dynamic variables and their fluctuations. If the impact of these variables is not considered and modelled accurately, the O&M contractor could incur significant losses undermining the viability of the EfW project. The latter employs a psychometric methodology to delve into the primary concerns of professionals involved in EfW projects regarding associated risks. The results show that common O&M risks identified in EfW PPP projects in UK and Canada prevail around unplanned maintenance, infeed waste reduction, market price, unsustainable debts, and policy changes.

Muhammad Shahid Nazir,1 Matloob Ahmad,1 Sana Aslam,2 Ayesha Rafiq,1 Sami A Al-Hussain,3 Magdi EA Zaki3 1Department of Chemistry, Government College University, Faisalabad, Pakistan; 2Department of Chemistry, Government College Women University, Faisalabad, Pakistan; 3Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi ArabiaCorrespondence: Matloob Ahmad; Magdi EA Zaki, Email Matloob.Ahmad@gcuf.edu.pk; mezaki@imamu.edu.saAbstract: The Coronavirus disease 2019 (COVID-19) pandemic is one of the most considerable health problems across the world. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the major causative agent of COVID-19. The severe symptoms of this deadly disease include shortness of breath, fever, cough, loss of smell, and a broad spectrum of other health issues such as diarrhea, pneumonia, bronchitis, septic shock, and multiple organ failure. Currently, there are no medications available for coronavirus patients, except symptom-relieving drugs. Therefore, SARS-CoV-2 requires the development of effective drugs and specific treatments. Heterocycles are important constituents of more than 85% of the physiologically active pharmaceutical drugs on the market now. Several FDA-approved drugs have been reported including molnupiravir, remdesivir, ritonavir, oseltamivir, favipiravir, chloroquine, and hydroxychloroquine for the cure of COVID-19. In this study, we discuss potent anti-SARS-CoV-2 heterocyclic compounds that have been synthesized over the past few years. These compounds included; indole, piperidine, pyrazine, pyrimidine, pyrrole, piperazine, quinazoline, oxazole, quinoline, isoxazole, thiazole, quinoxaline, pyrazole, azafluorene, imidazole, thiadiazole, triazole, coumarin, chromene, and benzodioxole. Both in vitro and in silico studies were performed to determine the potential of these heterocyclic compounds in the fight against various SARS-CoV-2 proteins. Keywords: COVID-19, SARS-CoV-2, heterocyclic nucleus, in vitro, in silico, molecular docking studies

Transformation of municipal solid waste into refuse-derived fuel (RDF) offers a promising solution for waste-to-energy conversion. In this context, a systematic literature review and scientometric analysis is conducted showing refinements in RDF application for energy-from-waste (EfW) initiatives, forging a comprehensive approach to sustainable waste management. Key aspects of EfW projects using RDF are examined, focusing on methodologies for calorific value estimation, waste characterization, quality assessment, and public–private partnerships (PPPs). Emphasis is placed on the necessity of accurate energy potential measurement and advanced characterization techniques, including computer vision, for effective waste sorting and analysis. Quality assessments of RDF are highlighted for their impact on decisions within the biomass fuel supply chain, emphasizing the importance of optimizing energy recovery. PPP’s are identified as key to successful execution of EfW projects, with their roles, trends, and risk modeling crucial for fostering effective collaboration between public and private sectors. The study concludes by identifying research gaps, such as the deficiency of new frameworks to support technical assessments of waste processing facilities for strategic, tactical, and operational improvements. Additionally, RDF applications in EfW are limited because of inconsistent waste sorting, deviation from specifications and environmental regulations. This approach aims to enhance sustainable waste management and energy recovery, guiding future research and implementation in the field.

Developing highly sensitive and selective non-enzymatic electrochemical biosensors for disease biomarker detection has become challenging in healthcare applications. However, advances in material science are opening new avenues for creating more dependable biosensing technologies. In this context, the present work introduces a novel approach by engineering a hybrid structure of zinc oxide nanorod (ZnO NR) modified with iron oxide nanoparticle (Fe2O3 NP) on an FTO electrode. This Fe2O3 NP-ZnO NR hybrid material functions as a nanozyme, facilitating the catalysis of cholesterol and enabling the direct transfer of electrons to the fluorine-doped tin oxide (FTO) electrode, limiting the need for costly and traditional enzymes in the detection process. This innovative non-enzymatic cholesterol biosensor showcases remarkable sensitivity, registering at 642.8 μA/mMcm2 within a linear response range of up to 9.0 mM. It also exhibits a low detection limit (LOD) of ∼12.4 μM, ensuring its capability to detect minimal concentrations of cholesterol accurately. Moreover, the developed biosensor displays exceptional selectivity by effectively distinguishing cholesterol molecules from other interfering biological species, while exhibiting outstanding stability and reproducibility. Our findings indicate that the Fe2O3 NP-ZnO NR hybrid nanostructure on the FTO electrode holds promise for enhancing biosensor stability. Furthermore, the present device fabrication platform offers versatility, as it can be adapted with various enzymes or modified with different metal oxides, potentially broadening its applicability in a wide range of biomarkers detection.

Abstract Genetic diversity plays a critical role in crop improvement. Radish is one of the important vegetable crops of Rabi season, however, there is a lack of information about radish germplasm resources available in Pakistan. The present research was carried out to characterize radish gene pool on the basis of qualitative and quantitative traits. Twelve genotypes of radish comprised of local and exotic sources were evaluated for genetic variability through morphological, quality, and quantitative traits. Analysis of variance depicted maximum variation among genotypes for all studied straits. High genotypic co-efficient of variance (GCV) value (>20%) in all traits indicates that these traits can be improved through selection. The estimate of genotypic co-efficient of variance (GCV) in present research was closer to phenotypic co-efficient of variance (PCV), suggesting the key role of genotype as compared to the environment. However, high heritability along with genetic advance (as % of mean) among studied genotypes suggests that the selection of studied traits simply on phenotypic basis can also be effective for the breeding program. Cluster analysis based on morphological traits, grouped genotypes into three clusters and cluster I, contained the maximum number of genotypes. In correlation analysis, significant positive correlation among plant fresh weight, root length, root weight, number of leaves and yield can be directly used a selection criteria. Studying of variability in qualitative traits through frequency distribution exhibited maximum variation for root shape and root base. This study showed considerable variability exists in studied genotypes that can further be used for the improvement of radish breeding.

While adhering to the unique principle of Islamic value, Islamic banks are subject to any occurring crisis, just like other banks in common. After the end of the COVID-19 crisis, it is important to determine whether Islamic banking can demonstrate its resilience based on the unique values it upholds. Therefore, the primary objective of this study is to capture how Islamic banks performed during the unprecedented challenge posed by COVID-19. By utilizing the data generated from financial reports, this study employs the RGEC (Risk Profile, Good Corporate Governance, Earnings, and Capital) method that was introduced by Indonesia Central Bank and SCnP (Sharia Conformity and Profitability) measurement. The findings show that by utilizing RGEC, the study classifies Islamic banks into tiers of health, distinguishing 7 as very healthy, 5 as healthy, and 2 as fairly healthy. Meanwhile, SCnP sheds light on the balance between Sharia conformity and profitability, highlighting 2 Islamic banks with commendable equilibrium, 5 displaying a tilt towards high profitability and low conformity, 4 with a tendency towards Shariah conformity with low profitability, and 3 struggling with challenges in both aspects. The study concludes the overall financial resilience of the majority of Islamic banks in Indonesia during the pandemic while noting that some banks faced challenges in achieving a harmonious balance between profitability and Sharia conformity.

Objective: To find out the prevalence of malnutrition in children under five years of age in a local population. Study Design: A descriptive cross-sectional study. Place and Duration of Study: Department of Paediatrics, Tehsil Headquarter Hospital Dargai, Malakand District from 1 July 2022 to 31 Dec 2022. Materials and Methods: A total of 156 children were included in the study. Demographic details like age, gender and weight were recorded. All children underwent malnutrition assessment as per operational definition using WHO Software. Data analysis was done using SPPS Version 24. Results: A total of 156 children were included in this research survey. Mean age and weight were 4.1±0.69 years & 12.8±0.94 kg respectively. A total of 51.28% children were between 3months to 3 years of age and 48.71% belonged to 4-5 years age group. Amongst them, 71.15% were males and 28.84% were females. Approximately 72.3% of the children were from families with a lower socio-economic status, while 19.23% and 8.33% were associated to the middle class and well of categories, respectively. Literacy rate of fathers was 44.87%. Regarding mothers, 85.25% were housewives, and 14.75% were employed. As per frequencies and percentages for malnutrition, 57 (36.35%) patients were recorded with malnutrition. Conclusion: Prevalence of child malnutrition is notably elevated among children below the age of five. This phenomenon is more commonly observed among children whose mothers are housewives, predominantly due to factors associated with limited educational attainment and constrained socioeconomic status.

Abstract Application of machine learning in plant breeding is a recent concept, that has to be optimized for precise utilization in the breeding program of high yielding crop plants. Identification and efficient utilization of heterotic grouping pattern aided with machine learning approaches is of utmost importance in hybrid cultivar breeding as it can save time and resources required to breed a new plant hybrid/variety. In the present study, 109 genotypes of sunflower were investigated at morphological, biochemical (SDS-PAGE) and molecular levels (through micro-satellites (SSR) markers) for heterotic grouping. All the three datasets were combined, scaled, and subjected to unsupervised machine learning algorithms, i.e., Hierarchical clustering, K-means clustering and hybrid clustering algorithm (hierarchical + K-means) for assessment of efficiency and resolution power of these algorithms in practical plant breeding for heterotic grouping identification. Following the application of machine learning unsupervised clustering approach, two major groups were identified in the studied sunflower germplasm, and further classification revealed six smaller classes in each major group through hierarchical and hybrid clustering approach. Due to high resolution, obtained in hierarchical clustering, classification achieved through this algorithm was further used for selection of potential parents. One genotype from each smaller group was selected based on the maximum seed yield potential and hybridized in a line × tester mating design producing 36 F1 cross combinations. These F1s along with their parents were studied in open field conditions for validating the efficacy of identified heterotic groups in sunflowers genetic material under study. Data for 11 agronomic and qualitative traits were recorded. These 36 F1 combinations were tested for their combining ability (General/Specific), heterosis, genotypic and phenotypic correlation and path analysis. Results suggested that F1 hybrids performed better for all the traits under investigation than their respective parents. Findings of the study validated the use of machine learning approaches in practical plant breeding; however, more accurate and robust clustering algorithms need to be developed to handle the data noisiness of open field experiments.

Enhancing the root traits through selection and breeding efforts can significantly improve the acquisition of soil resources, consequently boosting crop production in marginal environments. Cowpea is one of the important legume crops in arid and semi-arid regions, grown mostly in marginal environments under drought conditions. The roots play a crucial role in plant's ability to survive and adapt under such water stressed conditions. The understanding of cowpea genetic architecture related to root system architecture (RSA) remains limited due to difficulties in accurately measuring root traits. In the present study, a set 110 diverse mini-core collection of cowpea from IITA, Nigeria was evaluated for thirteen (13) different root-shoot traits under controlled conditions in the green house. Significant variation was recorded for all the root traits in the mini-core set. Most promising genotype for most of the root traits, viz., root volume, dry root weight, root biomass density and tap root diameter were identified. The correlation analysis revealed significant positive association (p < 0.001) of root weight with shoot weight (0.85), root volume (r = 0.61), and tap root diameter (0.56). In the principal component analysis (PCA), first three principal components explained the variance of 64.31%, with PC1 contributing 44.43%, PC2 10% and PC3 9.88%. All the root-shoot traits contribute positive variation to PC1especially dry root weight and dry shoot weight that had high component loading and in PC2 root shoot ratio had high loading. The root-shoot trait data was used with 6,574 good quality SNP data for discovery of genes/QTLs/marker-trait associations for root traits. The analysis of results of genome wide association study (GWAS) led to the identification of 52 unique SNPs associated significantly (p < 10−4) with thirteen root-shoot traits. Highest number of SNPs was found associated with root weight and chromosome 5 was identified as SNP hotspot for harboring majority of significant SNPs. We further delve candidate genes within 50 kb around the identified SNP that led to the identification of several important candidate genes underlying genomic regions responsible for root-shoot traits in cowpea. The promising cowpea genotypes for root traits and promising MTAs/candidate genes will prove useful in cowpea improvement programs including improvement of drought tolerance in cowpea.

Solar energy is largely dependent on weather conditions, resulting in unpredictable, fluctuating, and unstable photovoltaic (PV) power outputs. Thus, accurate PV power forecasts are increasingly crucial for managing and controlling integrated energy systems. Over the years, advanced artificial neural network (ANN) models have been proposed to increase the accuracy of PV power forecasts for various geographical regions. Hence, this paper provides a state-of-the-art review of the five most popular and advanced ANN models for PV power forecasting. These include multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and convolutional neural network (CNN). First, the internal structure and operation of these models are studied. It is then followed by a brief discussion of the main factors affecting their forecasting accuracy, including forecasting horizons, meteorological conditions, and evaluation metrics. Next, an in-depth and separate analysis of standalone and hybrid models is provided. It has been determined that bidirectional GRU and LSTM offer greater forecasting accuracy, whether used as a standalone model or in a hybrid configuration. Furthermore, hybrid and upgraded metaheuristic algorithms have demonstrated exceptional performance when applied to standalone and hybrid ANN models. Finally, this study discusses various limitations and shortcomings that may influence the practical implementation of PV power forecasting.

Poultry coccidiosis is an intestinal infection caused by an intracellular parasitic protozoan of the genus Eimeria. Coccidia-induced gastrointestinal inflammation results in large economic losses, hence finding methods to decrease its prevalence is critical for industry participants and academic researchers. It has been demonstrated that coccidiosis can be effectively controlled and managed by employing anticoccidial chemical compounds. However, as a result of their extensive use, anticoccidial drug resistance in Eimeria species has raised concerns. Phytochemical/herbal medicines (Artemisia annua, Bidens pilosa, and garlic) seem to be a promising strategy for preventing coccidiosis, in accordance with the “anticoccidial chemical-free” standards. The impact of herbal supplements on poultry coccidiosis is based on the reduction of oocyst output by preventing the proliferation and growth of Eimeria species in chicken gastrointestinal tissues and lowering intestinal permeability via increased epithelial turnover. This review provides a thorough up-to-date assessment of the state of the art and technologies in the prevention and treatment of coccidiosis in chickens, including the most used phytochemical medications, their mode of action, and the applicable legal framework in the European Union.

Abstract Broadly neutralizing antibodies (bnAbs) against HIV-1 target conserved envelope (Env) epitopes to block viral replication. Here, using structural analyses, we provide evidence to explain why a vaccine targeting the membrane-proximal external region (MPER) of HIV-1 elicits antibodies with human bnAb-like paratopes paradoxically unable to bind HIV-1. Unlike in natural infection, vaccination with MPER/liposomes lacks a necessary structure-based constraint to select for antibodies with an adequate approach angle. Consequently, the resulting Abs cannot physically access the MPER crawlspace on the virion surface. By studying naturally arising Abs, we further reveal that flexibility of the human IgG3 hinge mitigates the epitope inaccessibility and additionally facilitates Env spike protein crosslinking. Our results suggest that generation of IgG3 subtype class-switched B cells is a strategy for anti-MPER bnAb induction. Moreover, the findings illustrate the need to incorporate topological features of the target epitope in immunogen design.

We present a novel, systematic method for evaluating design printability in 3D food printing using a scoring system based on the Design for Additive Manufacturing (DfAM) guidelines. This study addresses a gap in the current literature by proposing a structured approach to assess and enhance the printability of 3D food designs. Our framework consists of a set of nine critical questions derived from the multi-level DfAM guidelines, focusing on key printability factors including unsupported features, geometric accuracy, and surface finish. The evaluation process converts qualitative assessments into numerical values, resulting in a comprehensive printability score that categorizes designs into high, moderate, or low printability levels. To validate the effectiveness of this method, we conducted a case study involving five different designs. The scoring system successfully explores the design space and maximizes the printability of 3D food products. This method alleviates the challenges in design evaluation compared with traditional trial-and-error approaches. The results demonstrate the practicality and efficiency of our framework’s output. The proposed methodology provides a structured approach to design evaluation, offering practical insights and a valuable tool for improving the success rate of 3D printed food products. This research contributes to the field by offering a systematic framework for assessing and enhancing the printability of 3D food designs, potentially accelerating the adoption and effectiveness of 3D food printing technology in various applications.

Kale is known for its exceptional nourishing and functional benefits to human body. However, it is an understudied species from genomic as well as agronomic aspects. It is important to characterize niche kale germplasms around the world to systematically conserve and utilize its genetic variability, especially for commercial traits in the interest of growers, consumers and industry. With this view, genomic and phenotypic characterizations of 62 Kashmiri kale accessions including popular landraces were done to estimate and partition genetic diversity, understand trait relationships, develop population structure and divulge marker-trait associations of economic significance. Sixty-six cross species microsatellite (SSR) markers within Brassica genus amplified 269 alleles in the germplasm. Their polymorphic information content (PIC) ranged from 0.00078 to 0.953 with an average of 0.407. The population structure analysis and neighbour joining tree clustering categorized the germplasm into three sub-populations. AMOVA revealed more within-population variance (67.73 %) than among-populations (32.27 %) variance. The principal component analysis (PCA) involving 24 agronomical traits revealed seven PCs (PC1 to PC7) having Eigen values more than 1, which explained a cumulative variation of 69.21 %. Association mapping with respect to these 24 agronomical traits using mixed linear model and general linear model revealed six overlapping significant marker-trait relationships with five being significant at probability value of 0.001/0.0001. The highly significant associations of two SSRs with economically important traits (siliqua length and seed weight) significantly correlated/related with leaf yield and seed yield were revealed for their possible utilization in marker assisted breeding for higher leaf and seed yields.

Cedrus deodara is the central conifer plant affected by ozone and nitrogen pollutants among forest species worldwide. The growth of C. deodara depends upon the ectomycorrhizal (ECM) association, which is usually disturbed by these factors. This study aims to understand how these factors affect plants at physiological and biochemical levels. Three fungal strain consortiums were inoculated with two-year-old C. deodara seedlings. The stresses of 100 kg N h−1and 100 ppb O3 were applied for six months to study their impact on chlorophyll and antioxidant enzymes (SOD, CAT, and APX). The results showed that C2 (Consortium of Cedrus deodara) positively impacted the growth of selected plant species. The high photosynthesis rate was determined by enhanced chlorophyll content, and C2-treated plants showed high chlorophyll content. Relatively, chlorophyll a and b contents increased significantly in the seedlings treated with Ethylenediurea (EDU) alone and with ozone stress. In addition, a significant difference was observed between EDU and O3-treated plants (14% EDU400-O3 and 23% EDU600-O3) and the control. Overall, antioxidant activities were higher in the treated samples than in the control. The order of SOD activity was C2 (448 U/gFW) and lowest (354.7 U/gFW) in control. APX also showed higher activity in treated plants in C1 ≥ C2 ≥ C3+O3, whereas CAT activity was the highest in C2 treatments. Ozone and nitrogen-stressed plants showed higher activities than EDU-treated plants compared to non-treated ones. Our findings highlight the importance of understanding the signaling effects of numerous precursors. Moreover, an extended investigation of seedlings developing into trees must be conducted to verify the potential of ectomycorrhizal strains associated with C. deodara and comprehend EDU's role as a direct molecular scavenger of reactive toxicants.

Remanufacturing of mechanical parts has recently gained much attention due to the rapid development of green technologies and sustainability. Recent efforts to automate the inspection step in the remanufacturing process using artificial intelligence are noticeable. In this step, a visual inspection of the end-of-life (EOL) parts is carried out to detect defective regions for restoration. This operation relates to the object detection process, a typical computer vision task. Many researchers have adopted well-known deep-learning models for the detection of damage. A common technique in the object detection field is transfer learning, where general object detectors are adopted for specific tasks such as metal surface defect detection. One open-sourced model, YOLOv7, is known for real-time object detection, high accuracy, and optimal scaling. In this work, an investigation into the YOLOv7 behavior on various public metal surface defect datasets, including NEU-DET, NRSD, and KolektorSDD2, is conducted. A case study validation is also included to demonstrate the model’s application in an industrial setting. The tiny variant of the YOLOv7 model showed the best performance on the NEU-DET dataset with a 73.9% mAP (mean average precision) and 103 FPS (frames per second) in inference. For the NRSD dataset, the model’s base variant resulted in 88.5% for object detection and semantic segmentation inferences. In addition, the model achieved 65% accuracy when testing on the KolektorSDD2 dataset. Further, the results are studied and compared with some of the existing defect detection models. Moreover, the segmentation performance of the model was also reported.

Salinity is known to have a greater impact on shoot growth than root growth. Na+ buildup in plant tissue under salt stress has been proposed as one of the main issues that causes growth inhibition in crops via ionic imbalances, osmotic stress and pH disturbances. However, the evidence for apoplastic Na+ buildup and the role of silicon in Na+ accumulation at the subcellular level is still enigmatic. The current study focuses on the accumulation of Na+ in the apoplast and symplast of younger and older leaves of two maize varieties (Iqbal as salt-tolerant and Jalal as salt-sensitive) using hydroponic culture along with silicon supplementation under short-term salinity stress. Subcellular ion analysis indicated that silicon nutrition decreased Na+ concentration in both apoplastic washing fluid and symplastic fluid of maize under salt stress. The addition of silicon under NaCl treatment resulted in considerable improvement in fresh biomass, relative water content, chlorophyll content, and concentration of important subcellular ions (i.e., Ca2+, Mg2+, and K+). Knowledge of subcellular ion analysis is essential for solving the mechanisms underlying vital cellular functions e.g. in the current study, the soluble Na+ concentration in the apoplast of older leaves was found to be significantly greater (36.1 mM) in the salt-sensitive variety under NaCl treatment, which was 42.4% higher when compared to the Na+ concentration in the salt-tolerant variety under the same treatment which can influence permeability of cell membrane, signal transduction pathways and provides insights into how ion compartmentalization can contributes to salt tolerance. Calcium silicate enrichment can contribute to increased growth and improved ionic homeostasis by minimizing leaf electrolyte leakage, improving mechanical functions of cell wall and reducing water loss, and improved photosynthetic function. In current investigation, increased water content and intracellular ionic homeostasis along with reduced concentration of Na+ in the maize leaf apoplast suggest that calcium silicate can be used to ameliorate the adverse effects of salt stress and obtain yield using marginal saline lands.

In recent years, we are facing the challenge of drug resistance emergence in fungi. The availability of limited antifungals and development of multi-drug resistance in fungal pathogens has become a serious concern in the past years in the health sector. Although several cellular, molecular, and genetic mechanisms have been proposed to explain the drug resistance mechanism in fungi, but a complete understanding of the molecular and genetic mechanisms is still lacking. Besides the genetic mechanism, epigenetic mechanisms are pivotal in the fungal lifecycle and disease biology. However, very little is understood about the role of epigenetic mechanisms in the emergence of multi-drug resistance in fungi, especially in Candida auris (C. auris). The current narrative review summaries the clinical characteristics, genomic organization, and molecular/genetic/epigenetic mechanisms underlying the emergence of drug resistance in C. auris. A very few studies have attempted to evaluate the role of epigenetic mechanisms in C. auris. Furthermore, advanced genetic tools such as the CRISP-Cas9 system can be utilized to elucidate the epigenetic mechanisms and their role in the emergence of multi-drug resistance in C. auris.

Abstract Hypoxic zones exist in solid tumors, where oxygen levels are significantly lower than in normal tissues. Hypoxia makes chemo-radiation therapeutics less effective and renders the metastatic potential more favorable. Emerging research has found that the transcriptional expression of hypoxia-inducible factor-1alpha (HIF-1α) promotes the transcription of vascular endothelial growth factor A (VEGF-A) and Hexokinase-I (HK-I), which are associated to cellular growth, angiogenesis, and metastatic invasion in many malignancies. However, it is still unclear whether VEGFA and HK-I expression has any influence on survival based on the intrinsic subtypes of breast cancer. Their prognostic significance remains a debatable topic. In the present study, quantitative Real-time polymerase chain reaction (qRT-PCR) was employed to check the relative expression of HIF-1α, VEGF-A and HK-I. The hazard ratios (HR) of breast cancer-specific and overall mortality were calculated using Cox proportional hazards model, which were adjusted for demographic, clinicopathological, and associated molecular variables, as well as the diagnosis year. The relative mRNA expression levels of HIF-1α (p = 0.0010) and VEGFA (p = 0.0119) were significantly higher in tumor tissues. The expression of both HIF-1α (p = 0.0111) and VEGFA (p = 0.0078) was higher in the TNBC group of breast cancers, while HK-I (p = 0.0106) was higher in ER/PR-positive, HER2-negative group. HIF-1α and HK-I overexpression were associated with a higher likelihood of survival, while overexpression of VEGFA was associated with a low survival rate, although it was not statistically significant.

Aquaponics, one of the vertical farming methods, is a combination of aquaculture and hydroponics. To enhance the production capabilities of the aquaponics system and maximize crop yield on a commercial level, integration of Industry 4.0 technologies is needed. Industry 4.0 is a strategic initiative characterized by the fusion of emerging technologies such as big data and analytics, internet of things, robotics, cloud computing, and artificial intelligence. The realization of aquaponics 4.0, however, requires an efficient flow and integration of data due to the presence of complex biological processes. A key challenge in this essence is to deal with the semantic heterogeneity of multiple data resources. An ontology that is regarded as one of the normative tools solves the semantic interoperation problem by describing, extracting, and sharing the domains’ knowledge. In the field of agriculture, several ontologies are developed for the soil-based farming methods, but so far, no attempt has been made to represent the knowledge of the aquaponics 4.0 system in the form of an ontology model. Therefore, this study proposes a unified ontology model, AquaONT, to represent and store the essential knowledge of an aquaponics 4.0 system. This ontology provides a mechanism for sharing and reusing the aquaponics 4.0 system’s knowledge to solve the semantic interoperation problem. AquaONT is built from indoor vertical farming terminologies and is validated and implemented by considering experimental test cases related to environmental parameters, design configuration, and product quality. The proposed ontology model will help vertical farm practitioners with more transparent decision-making regarding crop production, product quality, and facility layout of the aquaponics farm. For future work, a decision support system will be developed using this ontology model and artificial intelligence techniques for autonomous data-driven decisions.

Infectious diseases remain among the most pressing concerns for human health. This issue has grown even more complex with the emergence of multidrug-resistant (MDR) bacteria. To address bacterial infections, nanoparticles have emerged as a promising avenue, offering the potential to target bacteria at multiple levels and effectively eliminate them. In this study, silver nanoparticles (AA-AgNPs) were synthesized using the leaf extract of a medicinal plant, Abroma augusta. The synthesis method is straightforward, safe, cost-effective, and environment friendly, utilizing the leaf extract of this Ayurvedic herb. The UV-vis absorbance peak at 424 nm indicated the formation of AA-AgNPs, with the involvement of numerous functional groups in the synthesis and stabilization of the particles. AA-AgNPs exhibited robust antibacterial and antibiofilm activities against methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE). The MIC values of AA-AgNPs ranged from 8 to 32 μg/mL. Electron microscopic examination of the interaction of AA-AgNPs with the test bacterial pathogens showed a deleterious impact on bacterial morphology, resulting from membrane rupture and leakage of intracellular components. AA-AgNPs also demonstrated a dose-dependent effect in curtailing biofilm formation below inhibitory doses. Overall, this study highlights the potential of AA-AgNPs in the successful inhibition of both the growth and biofilms of MRSA and VRE bacteria. Following studies on toxicity and dose optimization, such AgNPs could be developed into effective medical remedies against infections.

Dioxins are highly toxic compounds that pose significant risks to human health and the environment. Burning plastics is a major source of dioxin emissions, contributing to their presence in the environment. This review paper presents a scientometric analysis of publications spanning four decades (1987-2023) aiming to investigate the relationship between plastics and dioxin emissions. Relevant scientific articles were retrieved from the Scopus database, and quantitative analyses were carried out using the VOSviewer tool. Data on literature coupling, keyword co-occurrence, authorship, and countries/regions activities were extracted, mapped, interpreted, and validated. Additionally, a systematic literature review was conducted to provide comprehensive insights into the existing research on smart sensing technologies and strategies employed to mitigate dioxin emissions from plastic waste incineration. The findings showed that the number of articles and publications on dioxin emissions from plastic waste has rapidly increased since 2005, indicating growing research interest. The main keywords used for the analysis included “air pollutants”, “waste management”, “mass spectrometry”, and “toxicity”. The results of this paper contribute to a broader understanding of the complex relationship between plastics and environmental degradation, facilitating informed decision-making and smart technology integration for a more sustainable future.

OBJECTIVE: To assess the diagnostic accuracy of raised serum uric acid level in females with pre-eclampsia, in predicting low birth weight. SUBJESTS & METHODS: Cross sectional study carried out at Gynecology Department, FGSH Poly Clinic, Islamabad & duration of study was 6 months from July 21, 2021 to Jan 20, 2022. A total of 225 Preeclampsia pregnant female were clinically examined and included in the study. Blood sample were collected for serum uric acid and followed till the birth of the baby. RESULTS: The study included age ranged from 18 up to 40 years. Average age was 28.69years +5.01SD. Sensitivity & specificity of uric acid level in Serum in diagnosis of low birth weight are 85.71% and 81.42% respectively while it has positive predictive & negative predictive value of 51.43% & 96.13% respectively. Diagnostic accuracy of uric acid level in serum was 82.22%. CONCLUSION: Uric acid level in serum is of great diagnostic and prognostic importance in women with pre-eclampsia & helps in predicting low birth weight.

The purpose of this study was to assess the impact of various concentrations of Bacillus licheniformis-fermented products (BLFP) on the growth and productivity of laying ducks (Anas platyrhynchos) subjected to heat stress during eight weeks of a feeding trial. A total of 150 one-day-old Brown Tsaiya ducks of both sexes were divided into five groups, with each group having three replicates and 10 ducks each for evaluation of growth performance. The treatment groups received dietary supplements of BLFP at levels of 0.1%, 0.2%, and 0.3%, along with a group receiving flavomycin (F) at 5 ppm, all over a 24-week period. The fermentation process in this study utilized a B. licheniformis strain (ATCC 12713) for the production of the spores through solid-state fermentation. The control group was given a basal diet consisting of yellow corn and soybean meal. The results showed that as compared to the flavomycin group, ducks in the 0.3% BLFP group had significantly higher body weights and better feed conversion rates. In addition, during the three weeks, the BLFP group showed higher feed consumption as compared to the control group. The jejunum villi length was significantly increased in the 0.2% BLPF group as compared to the control and flavomycin groups. This study also found that the flavomycin group had a significantly higher egg conversion rate, while the 0.1–0.3% BLFP groups had improved feed intake and the 0.3% group had significantly enhanced egg yolk color. Additionally, the 0.2% BLFP group showed substantial decreases in IL-1β, TNF-α, IL-6, and IL-10 levels in the liver as well as an uptick in the tight junction protein Occludin gene expression in the colon when compared to the control group. Furthermore, the expression of the heat shock protein 70 in the gut upregulated in the 0.1% and 0.2% BLFP groups. In conclusion, these observations demonstrate that dietary supplementation of 0.2% BLFP is an ideal concentration to increase gut morphology, alleviate inflammatory response, and promote gut integrity in heat-stressed laying ducks.

Wheat (Triticum aestivum L.) is an essential food crop in terms of consumption as well as production. Aflatoxin exposure has a widespread public health impact in economically developing nations, so there is a need to establish preventive techniques for these high-risk populations. Pre-harvest and post-harvest practices are the two strategies used to control aflatoxin contamination, which include the use of genetically modified crops that show resistance against Aspergillus infection, the use of pesticides, changing the planting and harvesting time of crops, and physical, chemical, and biological methods. In this research, aflatoxin detection and quantification were performed in different wheat varieties to determine quantitative differences in comparison to the European Commission’s limit of 4 ppb aflatoxins in wheat. TLC for qualitative and the ELISA kit method for quantitative analysis of aflatoxins were used. Out of 56 samples, 35 were found contaminated with aflatoxins, while the remaining 21 samples did not show any presence of aflatoxins. Out of the 35 contaminated samples, 20 samples showed aflatoxin contamination within the permissible limit, while the remaining 15 samples showed aflatoxin concentration beyond the permissible level, ranging from 0.49 to 20.56 ppb. After quantification, the nine highly contaminated wheat samples were detoxified using physical, chemical, and biological methods. The efficiency of these methods was assessed, and they showed a significant reduction in aflatoxins of 53–72%, 79–88%, and 80–88%, respectively. In conclusion, the difference in aflatoxin concentration in different wheat varieties could be due to genetic variations. Furthermore, biological treatment could be the method of choice for detoxification of aflatoxins in wheat as it greatly reduced the aflatoxin concentration with no harmful effect on the quality of the grains.

Nitrite monitoring serves as a fundamental practice for protecting public health, preserving environmental quality, ensuring food safety, maintaining industrial safety standards, and optimizing agricultural practices. Although many nitrite sensing methods have been recently developed, the quantification of nitrite remains challenging due to sensitivity and selectivity limitations. In this context, we present the fabrication of enzymeless iron oxide nanoparticle-modified zinc oxide nanorod (α-Fe2O3-ZnO NR) hybrid nanostructure-based nitrite sensor fabrication. The α-Fe2O3-ZnO NR hybrid nanostructure was synthesized using a two-step hydrothermal method and characterized in detail utilizing x-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). These analyses confirm the successful synthesis of an α-Fe2O3-ZnO NR hybrid nanostructure, highlighting its morphology, purity, crystallinity, and elemental constituents. The α-Fe2O3-ZnO NR hybrid nanostructure was used to modify the SPCE (screen-printed carbon electrode) for enzymeless nitrite sensor fabrication. The voltammetric methods (i.e., cyclic voltammetry (CV) and differential pulse voltammetry (DPV)) were employed to explore the electrochemical characteristics of α-Fe2O3-ZnO NR/SPCE sensors for nitrite. Upon examination of the sensor’s electrochemical behavior across a range of nitrite concentrations (0 to 500 µM), it is evident that the α-Fe2O3-ZnO NR hybrid nanostructure shows an increased response with increasing nitrite concentration. The sensor demonstrates a linear response to nitrite concentrations up to 400 µM, a remarkable sensitivity of 18.10 µA µM−1 cm−2, and a notably low detection threshold of 0.16 µM. Furthermore, its exceptional selectivity, stability, and reproducibility make it an ideal tool for accurately measuring nitrite levels in serum, yielding reliable outcomes. This advancement heralds a significant step forward in the field of environmental monitoring, offering a potent solution for the precise assessment of nitrite pollution.

In recent years, recycling of plastics has opened several doors of advancements in the field of additive manufacturing (AM). The process of fused deposition modelling (FDM) has already been utilized for reprocessing waste plastics into filaments and finally printing them into useful products. Yet another concept of extrusion additive manufacturing (EAM) is gaining lots of attention. In this work, a screw assisted system based on EAM is designed and installed alongside a pre-existing FDM system. This system is referred to as the direct FDM (DFDM) system throughout this work. The DFDM system used operates with a 1.75 mm nozzle and has the ability of giving a high throughput. The focus of this work is to use this hybrid system (combination of FDM and DFDM systems) to print both virgin as well as recycled plastics. The scope of this work is to use one technology (either FDM or DFDM) at a time and to use both simultaneously for multi-material printing in future. After several trials of printing and setting up some printing parameters, the proposed system has been able to print with virgin as well as recycled PLA.

Cross-laminated timber (CLT) has been one of the principal materials in mass timber construction, and now it is possible to find mid-rise and high-rise projects around the globe. This study makes a scientometric review comparison between CLT and the impact of the fourth industrial revolution (formally known as Industry 4.0) in the construction industry, focusing on worldwide academic publications between 2006 and 2022. The analysis considers keywords, co-author, co-citation, and clustering analysis. This study used 1320 documents, including journals and conference proceedings from the Scopus database, where 753 were for cross-laminated timber and 567 for Industry 4.0. Key researchers, research institutions, journals, publications, citation patterns, and trends are some of the results obtained from the scientometric analysis. Once the knowledge mapping was conducted for both fields, scrutiny of the interconnection of both areas was performed to find possible research gaps from a manufacturing perspective. Among the conclusions, it is logical to say that Industry 4.0 implementation in cross-laminated timber is still in its infancy. One of the most popular technologies impacting construction is the digital twin concept; however, no work is reported for CLT on this topic. Additionally, digital automation is a necessity in any research practice, and the use of industrial robots is shown to be an essential asset for CLT as these robots can handle complex shapes.

The development of antibiotic resistance in pathogenic bacterial strains has drawn attention to the quest for new natural antibacterial drugs. Therefore, in the present study, extracts of Rumex hastatus leaves were obtained in methanol and water, and R. hastatus-based silver nanoparticles (AgNPs) were synthesized. Structural and functional properties of synthesized silver nanoparticles were determined by UV–vis spectroscopy, XRD, FTIR and SEM. The synthesized AgNPs and crude extracts were tested to check their antibacterial potential against human pathogenic bacterial strains of Staphylococcus aureus, Staphylococcus haemoliticus, Bacillus cereus, Escherichia coli, Salmonella typhi and Pseudomonas aeruginosa in well diffusion and broth dilution methods. The present investigation has revealed for the first time that the broth dilution method was found more reproducible than that of the well diffusion method even at lower concentrations of AgNPs and crude extracts. UV– Vis spectroscopic analysis of AgNPs revealed a peak at 367 nm. XRD pattern showed a face-centered cubical to the spherical structure of AgNP crystals. FTIR analysis revealed that flavonoids and terpenoids are responsible for the reduction of AgNO3 to Ag+. SEM analysis determined the spherical structure and 51 nm average diameter of nanoparticles. The antibacterial activity of R. hastatus-based (AgNPs) was found to be significantly higher than aqueous plant extract and silver nitrate alone. Bacterial growth was inhibited by R. hastatus-based AgNPs in a dose-dependent manner. To our knowledge, silver nanoparticles (AgNPs) of R. hastatus were synthesized and characterized for the first time in this study and, based on the findings of current research work R. hastatus extract-based silver nanoparticles are suggested to be used as an antibacterial drug instead of synthetic drugs for the treatment of various human diseases/infections caused by the tested bacterial strains.

Software engineering extending Internet of Things (IoT) to the new extent and in return IoT reshaped the software industry. IoT refers to communicate the real time objects with world wide web. There is a great role of web services in the field of IoT. In the digital world it is the need of the hour to speak with the real time objects. To design such application web of things, provide a common platform for it. This research work analyzes the tools for the modeling of web services, web of things (WoT) and Internet of things. The main focus is to identify the modeling approaches of these technologies through literature review followed by Empirical Study. The study is aimed to improve the quality of the development process in terms of tools and modelling approaches. This research evaluates the tools. A model base approaches compared with the IoT Mashup tool in order to ensure the quality of IoT-based Systems.

Abstract Background Exosomes are nano-sized vesicles secreted by various cells into the intra and extracellular space and hence is an integral part of biological fluids including milk. In the last few decades, many research groups have proved the potential of milk exosomes as a sustainable, economical and non-immunogenic drug delivery and therapeutic agent against different pathological conditions. However, its anti-viral properties still remain to be unearthed. Methods Here, we have been able to isolate, purify and characterize the milk derived exosomes from Cow (CME) and Goat (GME) and further studied its antiviral properties against Dengue virus (DENV), Newcastle Disease Virus strain Komarov (NDV-K) and Human Immunodeficiency Virus (HIV-1) using an in-vitro infection system. Results TEM, NTA and DLS analysis validated the appropriate size of the isolated cow and goat milk exosomes (30–150 nm). Real-time PCR and immunoblotting results confirmed the presence of several milk exosomal miRNAs and protein markers. Our findings suggest that GME significantly decreased the infectivity of DENV. In addition, we confirmed that GME significantly reduces DENV replication and reduced the secretion of mature virions. Furthermore, heat inactivation of GME did not show any inhibition on DENV infection, replication, and secretion of mature virions. RNase treatment of GME abrogates the anti-viral properties indicating direct role of exosomes in DENV inhibition. In addition GME inhibited the infectivity of NDV-K, but not HIV-1, suggesting that the GME mediated antiviral activity might be virus specific. Conclusion This study demonstrates the anti-viral properties of milk exosomes and opens new avenues for the development of exosome-based therapies to treat viral diseases. Graphical Abstract

Quality standards have been a significant achievement for organizations worldwide. Environmental standards should be established in hospitals by coding ISO14001. Hospital water pollution can cause healthissues in nearby urban areas and settlements.The studywas aimed to estimate, characterize, use, and response to water quality parameters on amplification of environmental coordination at General Hospital Lahore (GHL), and their implications for evaluation in management reviewing. For these purposes, the drinking water samples were collected fromdifferent locations inthe study area. All these samples were analyzed fordifferent water quality parametersnamely, pH, TDS, turbidity, and chloride,and compared with nationalenvironmental quality standards (NEQS). All the resultsand management processeswere also compared with ISO 14001:2015,which indicatedthe weakness in the general administration frameworkand Environmental Management System (EMS).The results show that pH 8.6, TDS 1100 mg/l, turbidity 5.5 NTU, and chloride 260 mg/l were exceeding the maximum permissible limits. The present study recommended that GHL water should be treated before its use/ disposal. In the end, an EMS (a policy with protocol) manual was developed forcontinual improvement of water use. Drinking water quality assessment and EMSframework offer assistance to oversee the water quality and environmentin health centers. Drinking water quality management under the guidelines of the environment management system manual developed in this study will greatly help to manage the environment, particularly water pollution in local hospitals of Pakistan.

The material extrusion 3D printing process known as fused deposition modeling (FDM) has recently gained relevance in the additive manufacturing industry for large-scale part production. However, improving the real-time monitoring of the process in terms of its mechanical properties remains important to extend the lifespan of numerous critical applications. To enhance the monitoring of mechanical properties during printing, it is necessary to understand the relationship between temperature profiles and ultimate tensile strength (UTS). This study uses a cyber–physical production system (CPPS) to analyze the impact of four key thermal parameters on the tensile properties of polylactic acid (PLA). Layer thickness, printing speed, and extrusion temperature are the most influential factors, while bed temperature has less impact. The Taguchi L-9 array and the full factorial design of experiments were implemented along with the deposited line’s local fused temperature profile analysis. Furthermore, correlations between temperature profiles with the bonding strength during layer adhesion and part solidification can be stated. The results showed that layer thickness is the most important factor, followed by printing speed and extrusion temperature, with very close influence between each other. The lowest impact is attributed to bed temperature. In the experiments, the UTS values varied from 46.38 MPa to 56.19 MPa. This represents an increase in the UTS of around 17% from the same material and printing design conditions but different temperature profiles. Additionally, it was possible to observe that the influence of the parameter variations was not linear in terms of the UTS value or temperature profiles. For example, the increase in the UTS at the 0.6 mm layer thickness was around four times greater than the increase at 0.4 mm. Finally, even when it was found that an increase in the layer temperature led to an increase in the value of the UTS, for some of the parameters, it could be observed that it was not the main factor that caused the UTS to increase. From the monitoring conditions analyzed, it was concluded that the material requires an optimal thermal transition between deposition, adhesion, and layer solidification in order to result in part components with good mechanical properties. A tracking or monitoring system, such as the one designed, can serve as a potential tool for reducing the anisotropy in part production in 3D printing systems.

The losses in the power system vary the gap between energy supply and demand. Among these losses, the distribution system has major contributions compared to the transmission system. This paper thoroughly examines the PESCO distribution losses and implements a practical approach that forties in the loss’s minimization. Using the bifurcation and restoration strategies, the PESCO distribution system can be well-organized and an effective system can be designed. The proposed approach also helps in estimating the entailed capital investments and the rate of return over time. Both the HT (High Tension) and LT (Low Tension) distribution systems are considered for observation and the proposed systems are endorsed. Computer-based software, such as ELR (Energy Loss Reduction) and CADPAW (Computer-Aided Distribution Planning Work) is exploited to measure the results of both the proposed and existing system. After the restoration of a particular system, recommendations are concluded. The proposed approach applies to any existing power system.

Abstract Optimization of machining parameters like cutting speed, feed, and depth of cut is one of the extensively studied fields in the past two decades. While researchers agree optimization of these parameters is essential, there is no conscience as to what the objective of the optimization should be. The studies consider production cost, production time, surface finish, among others, as the objective of parameter optimization, but there are very few studies that consider the manufacturer prescribed tool life as the criteria for parament optimization. Among the methods that do consider tool life as an optimization objective, very few are closed-loop systems and these systems are facing challenges to generalizing when the application changes or the machining material changes or the tool geometry changes. Considering this, a novel image feedback using a convolution neural network-based method combined with principles of fuzzy logic is used to optimize machining parameters. Since the system is based on online feedback from the images of the inserts, it can be used for different materials, and the system is invariant to the different tool geometries and grades as the decisions are based on the wear mechanisms detected. The hybrid system is validated through experimentation for the turning application, but the methodology can be easily adapted for other machining applications.

IntroductionFortification of cereal products with natural plant extract is an interesting approach to fulfill the dietary requirement of the people.Materials and methodsPeels of pomegranate (rich source of natural compounds) were cut into small pieces and dried in three different methods such as solar drying (SOD), oven drying (OD), and sun drying (SUD). The fine powder was prepared and proximate compositions (protein, ash, moisture, fats, fiber, and carbohydrates), minerals (zinc, iron, calcium, and potassium), total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activity (DPPH) of the pomegranate peel powder (PP) were evaluated. Fine wheat flour (FWF) was fortified with different concentrations (3, 6, 8, 10, and 12 g) of PP powder, cookies were prepared and all the above analysis along with physical parameters (weight, width, thickness, spread ration) and sensory analysis were conducted. Cookies without PP powder were served as control.Results and discussionResults showed that a SOD was the best for drying PP powder in terms of compositional analysis. Addition of PP powder significantly (P < 0.05) enhanced the nutritional value, minerals profile and physical attributes of the fortified cookies. Sensory analysis of fortified cookies indicated that the cookies were acceptable to the sensory panel. Therefore, in conclusion, PP powder dried by SOD method could be used commercially in baking industries to provide nutritional enriched cookies to fulfill the dietary requirements of the people.

The emergence of resistant pathogenic bacterial strains has threatened the human beings and already developed remedial measures. Based on the traditional herbal therapeutic history, present study is aimed to assess in vitro and in silico inhibition potential of leaves extracts of Pinus roxburghii and Cedrus deodara against human pathogenic bacteria Staphylococcus aureus, Salmonella typhi and Pseudomonas aeruginosa. Hexane, methanol and acetone extracts of both plants were evaluated against above mentioned bacterial strains employing agar well diffusion technique. While docking analyses were performed to analyze the interaction of vital bioactive compounds and bacterial virulence proteins to get an idea about potential candidates for drug discovery. Both plant extracts exhibited greater antibacterial activities against S. aureus as compared to S. typhi and P. aeruginosa. The activity of different extracts also portrayed the role of polarity of solvent and compound to be extracted in each solvent i.e., activity of hexane extract > methanol > acetone with some variations. MIC (minimum inhibitory concentration) values of P. roxburghii extracts were less than that of C. deodara against tested strains, while variation was observed in MBCs (minimum bactericidal concentrations). Furthermore, molecular docking of studied plants bioactive compounds and bacterial proteins showed strong interactions (binding affinity) i.e., taxifolin > nortrachelogenin > bisabolene > valencene > caryophyllene. Antibacterial efficiencies of P. roxburghii and C. deodara suggested their application as effective therapeutic agents against diseases caused by mentioned bacterial strains. In silico analysis suggests the isolation and usage of bioactive components as potential antibacterial agents/drugs after further experimentations on animals.

Today, open channel structure is widely used in agricultural irrigation systems, fish breeding ponds, water purification or transfer systems, and streams. The biggest challenge when designing an open channel is to determine the overall shape of the channel and the connections and proper placement of the inlets and outlets so that the least turbulence and eddies are created in the channel in order to avoid issues such as increasing channel erosion, pressure on the walls, the creation of sludge and sewage due to stagnation of water in the canal, the reduction of water quality, the increase of polluting particles in the water, or even the breakage and deformation of the canal. In addition, parameters such as temperature, particle concentration, pressure, water level, etc. can be controlled inside the channel and are uniform along the channel path. The longer the channel and the more bends it has, the more difficult it is to place the branches. In this research, the uniform flow of fluid inside a channel with a 90° bend has been simulated, and its effective factors have also been studied. By adding lateral branches such to be widely used in various industries, it was proved that could be reduced, the circulation and disturbances generated by inflow by editing the shape of inlets and branches. The effects of circulation reduction on water flow after the bend were assessed and simulated. The flow simulation inside the channel was calculated with computational fluid dynamics; needless to mention that the channel flow had been assumed to be incompressible unsteady, and two-phase. Moreover, the flow was simulated based on the Naiver-Stokes equation and Re Normalization Group (RNG) model. The results showed that increasing the number of system inlets (meantime keeping the inflow and velocity of the inlet fluid constant) and the proper arrangement of each inlet greatly reduces the amount of turbulence in the flow at the inlet; in this case, the water flow gets uniform in a shorter path, and time period; as a result, the effects of sub-branches on the main channel would be negligible.

Background: Idiopathic sudden sensorineural hearing loss (ISSNHL) is commonly encountered in audiologic and otolaryngologic practice. Constraint-induced music/sound therapy (CIMT) is characterized by the plugging of the normal ear (constraint) and the simultaneous, stimulation of the affected ear with music, which is based on a well-established neurorehabilitation approach. Corticosteroid therapy (CST) is the current mainstay of treatment. The prognosis for hearing recovery depends on many factors including the severity of hearing loss, age, and presence of vertigo. Objective: To analyze the effectiveness of CIMT with CST in ISSNHL. Methods: We performed a systematic search, using specific keywords relevant to our study, in PubMed, Cochrane Central Register of Controlled Trials, and additional sources of published trials till December 2020. We then screened all search results obtained according to our inclusion/exclusion criteria and performed a quality assessment on all studies using the Newcastle–Ottawa scale and using MedCalc, a meta-analysis was performed on suitable studies. Results: The recovery rates of three included nonrandomized studies were assessed at 1 to 3 months. A total of 229 (CST: 131, CST + CIMT: 98) patients were pooled for meta-analysis. The meta-analysis using the random-effect model found the relative risk of recovery rate within 3 months to be 1.213 (95% confidence interval 0.709–2.074), a result that is not statistically significant. Conclusion: Although our analysis results do not demonstrate the noticeable effect of CIMT in ISSNHL, it can support be a gainful adjunct to CST for better hearing results than CST alone. Therefore, it needs further prospective randomized controlled multicenter trials with a large sample.

Security is one of the most critical aspects of software quality. Software security refers to the process of creating and developing software that assures the integrity, confidentiality, and availability of its code, data, and services. Software development organizations treat security as an afterthought issue, and as a result, they continue to face security threats. Incorporating security at any level of the Software Development Life Cycle (SDLC) has become an urgent requirement. Several methodologies, strategies, and models have been proposed and developed to address software security, but only a few of them give reliable evidence for creating secure software applications. Software security issues, on the other hand, have not been adequately addressed, and integrating security procedures into the SDLC remains a challenge. The major purpose of this paper is to learn about software security risks and practices so that secure software development methods can be better designed. A systematic literature review (SLR) was performed to classify important studies to achieve this goal. Based on the inclusion, exclusion, and quality assessment criteria, a total of 121 studies were chosen. This study identified 145 security risks and 424 best practices that help software development organizations to manage the security in each phase of the SDLC. To pursue secure SDLC, this study prescribed different security activities, which should be followed in each phase of the SDLC. Successful integration of these activities minimizing effort, time, and budget while delivering secure software applications. The findings of this study assist software development organizations in improving the security level of their software products and also enhancing their security efficiency. This will raise the developer’s awareness of secure development practices as well.

The number of security attacks and the impact has grown considerably in the recent several years. As a result, new emerging software development models are required that assist in developing software that is secure by default. This article reviews the most widely used security software models. It proposes a new Security Assurance Model (SAM) for Software Development that is adaptable to all contemporary scenarios, emphasizing global software development (GSD) vendor companies. The SAM of Software Development was developed after studying 11 well-known development models and analyzing results obtained from a systematic literature review (SLR) and questionnaire survey. The SAM of Software Development consists of seven security assurance levels: Governance and Security Threat Analysis, Secure Requirement Analysis, Secure Design, Secure Coding, Secure Testing and Review, Secure Deployment, and Security Improvement. The security assurance levels of SAM of software development consist of 46 critical software security risks (CSSRs) and 388 practices for addressing these risks. The proposed SAM of Software Development was assessed based on a tool created by Motorola, which is used to evaluate the present state of a company’s software processes and find areas for improvement. We conducted 3 case studies on software development companies, using data from real software projects to examine the results of a practical experiment in each company. The results of the case studies indicate that the proposed SAM of Software Development helps measure the security assurance level of an organization. In addition, it can potentially serve as a framework for researchers to develop new software security measures.

Agriculture is considered one of the most important sectors that play a strategic role in ensuring food security. However, with the increasing world's population, agri-food demands are growing — posing the need to switch from traditional agricultural methods to smart agriculture practices, also known as agriculture 4.0. To fully benefit from the potential of agriculture 4.0, it is significant to understand and address the problems and challenges associated with it. This study, therefore, aims to contribute to the development of agriculture 4.0 by investigating the emerging trends of digital technologies in the agricultural industry. For this purpose, a systematic literature review based on Protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses is conducted to analyse the scientific literature related to crop farming published in the last decade. After applying the protocol, 148 papers were selected and the extent of digital technologies adoption in agriculture was examined in the context of service type, technology readiness level, and farm type. The results have shown that digital technologies such as autonomous robotic systems, internet of things, and machine learning are significantly explored and open-air farms are frequently considered in research studies (69%), contrary to indoor farms (31%). Moreover, it is observed that most use cases are still in the prototypical phase. Finally, potential roadblocks to the digitization of the agriculture sector were identified and classified at technical and socio-economic levels. This comprehensive review results in providing useful information on the current status of digital technologies in agriculture along with prospective future opportunities.

Owing to the diagnostic dilemma, the prognosis of hepatocellular carcinoma (HCC) remains impoverished, contributing to the globally high mortality rate. Currently, HCC diagnosis depends on the combination of imaging modalities and the measurement of serum alpha-fetoprotein (AFP) levels. Nevertheless, these conventional modalities exhibit poor performance in detecting HCC at early stages. Thus, there is a pressing need to identify novel circulating biomarkers to promote diagnostic accuracy and surveillance. Circulating miRNAs are emerging as promising diagnostic tools in screening various cancers, including HCC. However, because of heterogenous and, at times, contradictory reports, the universality of miRNAs in clinical settings remains elusive. Consequently, we proposed to explore the diagnostic potential of ten miRNAs selected on a candidate-based approach in HCC diagnosis. The expression of ten candidate miRNAs (Let-7a, miR-15a, miR-26a, miR-124, miR-126, miR-155, miR-219, miR-221, miR-222, and miR-340) was investigated in serum and tissue of 66 subjects, including 33 HCC patients and 33 healthy controls (HC), by rt-PCR. Receiver operating characteristic curve (ROC) analysis was used to determine the diagnostic accuracy of the prospective serum miRNA panel. To anticipate the potential biological roles of a three-miRNA signature, the target genes were evaluated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway. The serum and tissue expression of miRNAs (Let-7a, miR-26a, miR-124, miR-155, miR-221, miR-222, and miR-340) were differentially expressed in HCC patients (p < 0.05). The ROC analysis revealed promising diagnostic performance of Let-7a (AUC = 0.801), miR-221 (AUC = 0.786), and miR-2 (AUC = 0.758) in discriminating HCC from HC. Furthermore, in a logistic regression equation, we identified a three-miRNA panel (Let-7a, miR-221, and miR-222; AUC = 0.932) with improved diagnostic efficiency in differentiating HCC from HC. Remarkably, the combination of AFP and a three-miRNA panel offered a higher accuracy of HCC diagnosis (AUC = 0.961) than AFP alone. The functional enrichment analysis demonstrated that target genes may contribute to pathways associated with HCC and cell-cycle regulation, indicating possible crosstalk of miRNAs with HCC development. To conclude, the combined classifier of a three-miRNA panel and AFP could be indispensable circulating biomarkers for HCC diagnosis. Furthermore, targeting predicted genes may provide new therapeutic clues for the treatment of aggressive HCC.

Autonomous docking and recharging are among the critical tasks for autonomous mobile robots that work continuously in manufacturing environments. This requires robots to demonstrate the following abilities: (i) detecting the charging station, typically in an unstructured environment and (ii) autonomously docking to the charging station. However, the existing research, such as that on infrared range (IR) sensor-based, vision-based, and laser-based methods, identifies many difficulties and challenges, including lighting conditions, severe weather, and the need for time-consuming computation. With the development of deep learning techniques, real-time object detection methods have been widely applied in the manufacturing field for the recognition and localization of target objects. Nevertheless, those methods require a large amount of proper and high-quality data to achieve a good performance. In this study, a Hikvision camera was used to collect data from a charging station in a manufacturing environment; then, a dataset for the wireless charger was built. In addition, the authors of this paper propose an autonomous docking and recharging method based on the deep learning model and the Lidar sensor for a mobile robot operating in a manufacturing environment. In the proposed method, a YOLOv7-based object detection method was developed, trained, and evaluated to enable the robot to quickly and accurately recognize the charging station. Mobile robots can achieve autonomous docking to the charging station using the proposed Lidar-based approach. Compared to other methods, the proposed method has the potential to improve recognition accuracy and efficiency and reduce the computation costs for the mobile robot system in various manufacturing environments. The developed method was tested in real-world scenarios and achieved an average accuracy of 95% in recognizing the target charging station. This vision-based charger detection method, if fused with the proposed Lidar-based docking method, can improve the overall accuracy of the docking alignment process.

Background: Ultrasound guided transvers abdominis plane (TAP) block is a recent modality of managing postoperative pain in lower abdominal surgeries. Aims and Objective: This study was carried out to evaluate postoperative analgesic effectiveness and opioid sparing effect of ultrasound guided transverse abdominis plane block in total abdominal hysterectomy. Materials and Methods: One hundred patients of age group 25 to 65 years enrolled for total abdominal hysterectomy under general anesthesia were studied in the postoperative period at Lal Ded Hospital, Government Medical College, Srinagar over a period of 18 months. Patients who received injection Ropivacaine by ultrasound guided TAP were kept in Group A and others who received only conventional analgesia were kept in Group B. Postoperative pain scores were assessed by VAS score at 1,2,3,4,5,6,12,18 and 24 hours. Time of first rescue analgesia, total opioid consumption and postoperative adverse events were also observed. Results: Postoperative VAS pain scores were significantly reduced at all time intervals in TAP bock group (Group A) when compared with conventional analgesia group (Group B). Mean time for first rescue analgesia was 377 min in Group A as compared to 169 minutes Group B and the difference was statistically significant (p

Inspection processes are becoming more and more popular beyond the manufacturing industry to ensure product quality. Implementing inspection systems in multistage production lines brings many benefits in productivity, quality, and customer satisfaction. However, quantifying the changes necessary to adapt the production to these systems is analytically complicated, and the tools available lack the flexibility to visualize all the inspection strategies available. This paper proposed a discrete-event simulation model that relies on probabilistic defect propagation to quantify the impact on productivity, quality, and material supply at the introduction of inspection processes in a multistage production line. The quantification follows lean manufacturing principles, providing from quite basic quantity and time elements to more comprehensive key performance indicators. The flexibility of discrete-event simulation allows for customized manufacturing and inspection topologies and variability in the tasks and inspection systems used. The model is validated in two common manufacturing scenarios, and the method to analyze the cost-effectiveness of implementing inspection processes is discussed.